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Redesigning Assessments for AI-Enhanced Learning: A Framework for Educators in the Generative AI Era

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Publicado en:Education Sciences vol. 15, no. 2 (2025), p. 174
Autor principal: Khlaif, Zuheir N
Otros Autores: Wejdan Awadallah Alkouk, Salama, Nisreen, Belal Abu Eideh
Publicado:
MDPI AG
Materias:
Teaching
Higher education
Collaboration
Instructional design
Student participation
Adaptation
Automation
Ethics
Feedback
Generative artificial intelligence
Teachers
Chatbots
Skills
Sustainable development
Personalized learning
Decision making
Learning analytics
Creativity
Literature reviews
Critical thinking
Authentic Learning
Educational Quality
Influence of Technology
Active Learning
Negative Attitudes
Learning Processes
Class Size
Interpersonal Relationship
Professional Development
Artificial Intelligence
Graduate Study
Decision Making Skills
Educational Assessment
Outcomes of Education
Problem Solving
Learner Engagement
Constructivism (Learning)
Educational Strategies
Educational Principles
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Resumen:The emergence of generative artificial intelligence (Gen AI) in education offers both opportunities and challenges, particularly in the context of student assessment. This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose. A qualitative methodology was employed, gathering data through semi-structured interviews and focus groups, along with examples of redesigned assessments. Sixty-one faculty members participated in the study, and the data were analyzed using both deductive and inductive thematic approaches. Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies. However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns. The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era. Based on these insights, the study developed a conceptual framework titled “Against, Avoid, Adopt, and Explore”. Future research is needed to validate this framework and further refine its application in educational contexts.
ISSN:2227-7102
2076-3344
DOI:10.3390/educsci15020174
Fuente:Education Database